Heavy metal contamination in landfill-impacted urban water systems: a GIS-based WQI and spatial risk assessment in Dhaka, Bangladesh

Urban landfill activities can significantly alter the quality of nearby water systems through the release of metal-enriched leachate, raising concerns for environmental sustainability and public health in rapidly urbanizing regions. This study assesses heavy metal contamination in urban water systems in Dhaka, Bangladesh, using an integrated approach that combines Water Quality Index (WQI), GIS-based spatial analysis, and exploratory data-driven modelling. Twelve water samples were collected from three major landfill-influenced zones (Matuail, Amin Bazar, and Kodda) during a post-monsoon field campaign in October 2025. Samples were analyzed for key physicochemical parameters and selected heavy metals, including iron (Fe), manganese (Mn), and lead (Pb). Water quality conditions were evaluated using WQI, while spatial mapping was applied to identify contamination patterns and high-risk zones associated with landfill proximity. Exploratory statistical and machine-learning-assisted analyses were used only to examine potential inter-parameter relationships under severe data constraints. WQI values ranged from 45 to 185, with 67% of sampling sites classified as poor to unsuitable for drinking purposes. Spatial analysis revealed pronounced contamination gradients radiating from landfill zones, with metal concentrations generally decreasing with distance from waste-disposal sites. Lead emerged as the most critical concern, exceeding the WHO guideline value (0.01 mg/L) at 75% of sampling locations, with a maximum concentration of 0.18 mg/L in landfill-proximal zones. Iron and manganese showed exceedance rates of 58% and 50%, respectively, reflecting mixed geogenic and anthropogenic influences. Although predictive performance of the data-driven models was constrained by the limited sample size (Model performance indicated limited predictive generalization under data constraints, while still providing useful insight into controlling factors), exploratory feature-importance analysis indicated that electrical conductivity, organic load (BOD, COD), and site-proximity factors were among the most influential variables in model outputs under data-limited conditions; these patterns should be interpreted as preliminary signals rather than confirmed causal controls. Overall, the findings indicate that integrated WQI-GIS assessment, supported by exploratory modelling, provides a preliminary basis for contamination hotspot screening and targeted water quality monitoring in landfill-impacted urban environments. The findings should be interpreted with caution due to the limited sample size ( n = 12), single-season sampling, and uncertainty associated with spatial interpolation. The study highlights lead contamination as a priority public-health concern requiring immediate monitoring and mitigation in Dhaka’s landfill-adjacent communities.